Age is more than just a number: implications for an aging workforce in the US transportation sector

The US workforce is aging. At the same time, there are a record number of open positions in the transportation sector, which has traditionally been a well-paying, but stressful and schedule-dependent, occupation. Due to increasing longevity, need, and ability to work, a possible solution to the transportation workforce shortfall may lie within the retention and recruitment of older workers. This paper uses a socio-technical framework to examine the pertinent, though scant, literature and data related to older workforce demographics and operational needs, the regulatory environment, requisite knowledge, skills and abilities, and application of support technology and training. Although there is evidence of age-related changes in physiology and cognition, the current science remains unable to resolve how an older workforce may most appropriately be applied to transportation to maximize system safety and minimize negative impact to worker well-being.     

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.     

Redox proteomics identification of 4‐hydroxynonenal‐modified brain proteins in Alzheimer's disease: Role of lipid peroxidation in Alzheimer's disease pathogenesis

Numerous studies have shown that neuronal lipids are highly susceptible to oxidative stress including in those brain areas directly involved in the neurodegenerative process of Alzheimer's disease (AD). Lipid peroxidation directly damages membranes and also generates a number of secondary biologically active products (toxic aldehydes)that are capable of easily attacking lipids, proteins, and DNA. Accumulating evidence has demonstrated regionally increased brain lipid peroxidation in patients with AD; however, extensive studies on specific targets of lipid peroxidation‐induced damage are still missing. The present study represents a further step in understanding the relationship between oxidative modification of protein and neuronal death associated with AD. We used a proteomics approach to determine specific targets of lipid peroxidation in AD brain, both in hippocampus and inferior parietal lobule, by coupling immunochemical detection of 4‐hydroxynonenal‐bound proteins with 2‐D polyacrylamide gel electrophoresis and MS analysis. We identified 4‐hydroxynonenal‐bound proteins in the hippocampus and inferior parietal lobule brain regions of subjects with AD. The identified proteins play different biological functions including energy metabolism, antioxidant system, and structural proteins, thus impairing multiple molecular pathways. Our results provide further evidence for the role of lipid peroxidation in the pathogenesis of AD.     

A graph-based approach for extracting terminological properties from information sources with heterogeneous formats

The problem of handling both the integration and the cooperation of a large number of information sources characterised by heterogeneous representation formats is a challenging issue. In this context, a central role can be played by the knowledge about the semantic relationships holding between concepts belonging to different information sources (intersource properties). In this paper, we propose a semiautomatic approach for extracting two kinds of intersource properties, namely synonymies and homonymies, from heterogeneous information sources. In order to carry out the extraction task, we introduce both a conceptual model, for representing involved sources, and a metrics, for measuring the strength of the semantic relationships holding among concepts represented within the same source.     

A Technique for Extracting Sub-source Similarities from Information Sources Having Different Formats

In this paper we propose a semi-automatic technique for deriving the similarity degree between two portions of heterogeneous information sources (hereafter, sub-sources). The proposed technique consists in two phases: the first one selects the most promising pairs of sub-sources, whereas the second one computes the similarity degree relative to each promising pair. We show that the detection of sub-source similarities is a special case (and a very interesting one, for semi-structured information sources) of the more general problem of Scheme Match. In addition, we present a real example case to clarify the proposed technique, a set of experiments we have conducted to verify the quality of its results, a discussion about its computational complexity and its classification in the context of related literature. Finally, we discuss some possible applications which can benefit by derived similarities.     

Controllability of One Spin and Two Interacting Spins

Mathematics of Control, Signals, and Systems - ?We consider the problem of steering control for the systems of one spin ½ particle and two interacting spin ½ particles in an...     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

A control oriented model for manual-pick warehouses

Nowadays the low cost of wireless communications allows the real time monitoring of the state of manual-pick warehouse systems making possible the real time control of these systems. At this aim, an approach based on a two-level model is presented in this paper. At the lower level, Petri nets are used to build online a model representing the active missions state and to detect conflicts among resources. At the upper level, logical expressions are used to add constraints for a single mission or item. The model is proved to be effective for online monitoring, scheduling and rescheduling of warehouse activities.     

A Neural Network Model of Caenorhabditis Elegans: The Circuit of Touch Sensitivity

The paper presents a neural network model of the touch sensitivity circuit of the nematode Caenorhabditis elegans. We describe a serie of simulations in which neural networks are trained, using a genetic algorithm, to reproduce the habituation of the nematode's touch sensitive behavior. A lesion study of the network allows to make a direct comparison between the fine functioning of the model and the data collected in real organisms. The model accords well with the known neurobiological data and it suggests some hypotheses about the functioning of the neural circuit and of single neurons.